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  • Detection of clustered anomalies in single-voxel morphometry as a rapid automated method for identifying intracranial aneurysms

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    Alonso-Caneiro958646-Accepted.pdf (4.161Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Allenby, MC
    Liang, ES
    Harvey, J
    Woodruff, MA
    Prior, M
    Winter, CD
    Alonso-Caneiro, D
    Griffith University Author(s)
    Alonso-Caneiro, David
    Year published
    2021
    Metadata
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    Abstract
    Unruptured intracranial aneurysms (UIAs) are prevalent neurovascular anomalies which, in rare circumstances, rupture to cause a catastrophic subarachnoid haemorrhage. Although surgical management can reduce rupture risk, the majority of UIAs exist undiscovered until rupture. Current clinical practice in the detection of UIAs relies heavily on manual radiological review of standard imaging modalities. Recent computer-aided UIA diagnoses can sensitively detect and measure UIAs within cranial angiograms but remain limited to low specificities whose output also requires considerable radiologist interpretation not amenable to ...
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    Unruptured intracranial aneurysms (UIAs) are prevalent neurovascular anomalies which, in rare circumstances, rupture to cause a catastrophic subarachnoid haemorrhage. Although surgical management can reduce rupture risk, the majority of UIAs exist undiscovered until rupture. Current clinical practice in the detection of UIAs relies heavily on manual radiological review of standard imaging modalities. Recent computer-aided UIA diagnoses can sensitively detect and measure UIAs within cranial angiograms but remain limited to low specificities whose output also requires considerable radiologist interpretation not amenable to broad screening efforts. To address these limitations, we have developed a novel automatic pipeline algorithm which inputs medical images and outputs detected UIAs by characterising single-voxel morphometry of segmented neurovasculature. Once neurovascular anatomy of a specified resolution is segmented, correlations between voxel-specific morphometries are estimated and spatially-clustered outliers are identified as UIA candidates. Our automated solution detects UIAs within magnetic resonance angiograms (MRA) at unmatched 86% specificity and 81% sensitivity using 3 min on a conventional laptop. Our approach does not rely on interpatient comparisons or training datasets which could be difficult to amass and process for rare incidentally discovered UIAs within large MRA files, and in doing so, is versatile to user-defined segmentation quality, to detection sensitivity, and across a range of imaging resolutions and modalities. We propose this method as a unique tool to aid UIA screening, characterisation of abnormal vasculature in at-risk patients, morphometry-based rupture risk prediction, and identification of other vascular abnormalities.
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    Journal Title
    Computerized Medical Imaging and Graphics
    Volume
    89
    DOI
    https://doi.org/10.1016/j.compmedimag.2021.101888
    Copyright Statement
    © 2021 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Biomedical engineering
    Cerebral angiography
    Computational anatomy
    Intracranial aneurysm
    Statistical shape analysis
    Publication URI
    http://hdl.handle.net/10072/412513
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    • Journal articles

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